{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Introduction to Radar Using Python and MATLAB***\n",
    "## Andy Harrison - Copyright (C) 2019 Artech House\n",
    "<br/>\n",
    "\n",
    "# Sensitivity Time Control\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Referring to Section 5.5, the strength of the radar return signal can vary widely.  This variation often exceeds the dynamic range of fixed gain systems.  One method for overcoming this limitation is through the use of gain control.  The first type of gain control considered is STC.  For targets of constant radar cross section, their return signal strength decreases with range as $1/r^4$, where $r$ is the range from the radar to the target.  Decreasing the attenuation as a function of $1/r^4$ would maintain a return signal of constant amplitude versus range.  Surface clutter tends to decrease as $1/r^3$ \\cite{GTRadar}.  Therefore, the STC attenuator is set to reduce the attenuation as $1/r^{3.5}$ to provide a good compromise between the targets and clutter.  STC may be used with low and medium pulse repetition frequency (PRF) waveforms, such as those used for search and track, but not with high PRF waveforms as distant targets competing with nearby clutter would be attenuated.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin by setting the library path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lib_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the pulse repetition frequency (Hz) and the pulse width (s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pulse_repetition_frequency = 30e3\n",
    "\n",
    "pulsewidth = 1e-6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the STC attenuation using the `sensitivity_time_control` routines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Libs.receivers import sensitivity_time_control\n",
    "\n",
    "receive_range, atten = sensitivity_time_control.attenuation(pulse_repetition_frequency, pulsewidth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the routines from `matplotlib` display the normalized STC attenuation (dB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from numpy import log10, array\n",
    "\n",
    "\n",
    "# Set the figure size\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 10)\n",
    "\n",
    "\n",
    "# Display the results\n",
    "\n",
    "plt.plot(receive_range, 10.0 * log10(array(atten) / max(atten)), '')\n",
    "\n",
    "\n",
    "# Set the plot title and labels\n",
    "\n",
    "plt.title('Sensitivity Time Control', size=14)\n",
    "\n",
    "plt.xlabel('Range (m)', size=12)\n",
    "\n",
    "plt.ylabel('Normalized Attenuation (dB)', size=12)\n",
    "\n",
    "\n",
    "# Set the tick label size\n",
    "\n",
    "plt.tick_params(labelsize=12)\n",
    "\n",
    "\n",
    "# Turn on the grid\n",
    "\n",
    "plt.grid(linestyle=':', linewidth=0.5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
